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United States Patent |
5,574,466
|
Reed
,   et al.
|
November 12, 1996
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Method for wireless communication system planning
Abstract
A method for wireless communication system planning includes, in a first
embodiment, determining an image tree (500), based on a transmitter
location (401) and the reflective (415) and diffractive (425) surfaces
within a coverage region, and limiting the image tree to exclude branching
for higher order images requiring more than a predetermined number of
reflections and/or diffractions, or potential child images corresponding
to surfaces not within the scope of the parent image (530, 560). Based on
the image tree and propagation path back-tracing (620) a received signal
quality measure (e.g., power) is determined for each transmit location. By
comparing the different received signal powers an optimal receiver unit
location is determined. Further, by back-tracing for further antenna
locations/combinations, and comparing for diversity effects (864, 865),
overall coverage qualities can be determined for each antenna combination
and compared to yield optimal base diversity antenna locations (867).
Inventors:
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Reed; John D. (Arlington, TX);
Tang; Yuqiang (Plano, TX)
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Assignee:
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Motorola, Inc. (Schaumburg, IL)
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Appl. No.:
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452799 |
Filed:
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May 30, 1995 |
Current U.S. Class: |
342/359; 342/350; 343/703; 455/277.1; 455/277.2 |
Intern'l Class: |
G01S 001/00 |
Field of Search: |
343/703
342/360,173,350,359
455/277.1,277.2
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References Cited
U.S. Patent Documents
4704734 | Nov., 1987 | Menich et al. | 455/33.
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4978962 | Dec., 1990 | Hisada et al. | 342/351.
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5313660 | May., 1994 | Lindemeier et al. | 455/135.
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5335010 | Aug., 1994 | Lindemeier et al. | 348/706.
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5396255 | Mar., 1995 | Durkota et al. | 342/360.
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Other References
Henry L. Bertoni, Fellow, IEEE, Walter Honcharenko, Member, IEEE, Leandro
Rocha Maciel, and Howard H. Xia, "UHF Propagation Prediction for Wireless
Personal Communications", Proceedings of the IEEE, Sep. 1994, pp.
1333-1359.
Jorgen Bach Andersen, Theodore S. Rappaport, and Susumu Yoshida,
"Propagation Measurements and Models for Wireless Communications
Channels", IEEE Communications, Jan. 1995 vol. 33 No. 1 pp. 42-49.
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Primary Examiner: Barron, Jr.; Gilberto
Assistant Examiner: Phan; Dao L.
Attorney, Agent or Firm: Sonnentag; Richard A.
Parent Case Text
RELATED APPLICATIONS
The present application is continuation in part of U.S. patent application
Ser. No. 08/415,051, to Tang et al., filed Mar. 31, 1995.
Claims
We claim:
1. A method of determining where to locate diversity antennas of a
communication unit, comprising in an automated digital processor:
(a) selecting a set of candidate antenna locations from a group of
locations within a coverage area;
(b) selecting a set of transmit locations from the group of locations;
(c) determining a set of receive signal quality measures for each candidate
antenna location, of the set of candidate antenna locations, by ray
tracing between said each candidate antenna location and the set of
transmit locations based on predetermined signal characteristics;
(d) determining a set of diversity signal quality measures, for each
combination of candidate antenna locations from a predetermined group of
combinations of the candidate antenna locations, by diversity processing
each set of receive signal quality measures of all candidate antenna
locations of said each combination; and
(e) determining a coverage quality measure for each set of diversity signal
quality measures of said each combination.
2. The method of claim 1, further comprising:
(f) comparing each coverage quality measure to determine a first
combination of said each combination having a greatest coverage quality
measure, and storing corresponding antenna locations of said first
combination.
3. The method of claim 1, wherein step (e) further comprises determining a
coverage quality measure for each set of diversity signal quality measures
of said each combination based on at least one predetermined coverage
criteria.
4. The method of claim 1, wherein step (e) further comprises determining a
coverage quality measure for each set of diversity signal quality measures
of said each combination based on predetermined coverage criteria
comprising a predetermined interference level and minimum C/I (carrier to
interference) level, wherein the set of receive signal quality measures is
a set of estimated receive signal strengths, and the coverage quality
measure is determined by each measure of the set of diversity signal
quality measures being divided by the predetermined interference level to
form a set of C/I measures, and comparing the set of C/I measures with the
minimum C/I level to output a set of low-signal locations of the set of
transmit locations and corresponding low-signal C/I measures as the
coverage quality measure.
5. The method of claim 1, wherein step (e) further comprises determining a
coverage quality measure for each set of diversity signal quality measures
of said each combination based on predetermined coverage criteria
comprising a predetermined interference level and minimum C/I (carrier to
interference) level, wherein the set of receive signal quality measures is
a set of estimated receive signal strengths, and the coverage quality
measure is determined by each measure of the set of diversity signal
quality measures being divided by the predetermined interference level to
form a set of C/I measures, and comparing the set of C/I measures with the
minimum C/I level to determine a set of low-signal locations of the set of
transmit locations, and determining a ratio of low-signal locations to a
subset of the set of transmit locations all within a predetermined range
of said each combination of candidate antenna locations, said ratio being
the coverage quality measure.
6. The method of claim 5, wherein step (e) further comprises determining a
coverage quality measure for each set of diversity signal quality measures
of said each combination based on predetermined coverage criteria
comprising a predetermined interference level, wherein the predetermined
interference level includes noise interference.
7. The method of claim 1, wherein the predetermined group of combinations
is a group comprising all combinations of a first candidate antenna
location with all remaining candidate antenna locations of the set of
candidate antenna locations.
8. The method of claim 1, wherein the predetermined signal characteristics
comprise a transmit power level and a frequency.
9. The method of claim 8, further comprising repeating steps (a) through
(e) for a further frequency to determine a further coverage quality
measure for each set of diversity signal quality measures using the
further frequency; and
(f) determining a first combination of said each combination having a
greatest coverage quality measure, the a greatest coverage quality measure
being determined from each coverage quality measure and each further
coverage quality measure, and storing corresponding antenna locations of
said first combination.
10. A method of determining a quality of candidate locations for diversity
antennas of a communication unit, comprising:
(a) providing a known environment and selecting plural combinations of
candidate antenna locations and a set of transmit locations;
(b) measuring, for each candidate of the candidate antenna locations, a
respective set of receive signal quality measures for rays between said
each candidate and the set of transmit locations; and
(c) determining, for each combination of the plural combinations of
candidate antenna locations, a set of diversity signal quality measures by
diversity processing the respective set for each candidate of the
candidate antenna locations forming part of said each combination.
11. The method of claim 10, further comprising:
(d) determining a coverage quality measure for each combination based on
each set of diversity signal quality measures.
12. The method of claim 11, comprising performing steps (a) through (d) in
an automated digital processor, and further comprising:
(e) selecting a best combination of the plural combinations based on the
coverage quality measure for each combination.
13. The method of claim 12, wherein step (c) comprises measuring based on
ray tracing between said each candidate and the set of transmit locations.
14. The method of claim 13, wherein the step of providing the known
environment comprises accessing a memory including data about each of
plural reflective surfaces and each of plural diffractive surfaces of the
known environment, and the step of ray tracing comprises:
(i) determining a first image tree for a first transmit location within the
known environment, having a predetermined scope, by:
(A) determining first order images of the first image tree by determining,
for each of the plural reflective surfaces and each of the plural
diffractive surfaces, respectively, within the predetermined scope, a
first order image and a scope;
(B) determining second and higher order images of the image tree by
repeating step (b)(i), for a predetermined number of reflections and
diffractions, such that for each of the plural reflective surfaces and
plural diffractive surfaces, respectively, a next order image and a scope
of the next order image is determined;
(ii) selecting a first candidate antenna location and back-tracing from the
first candidate antenna location using the first image tree to determine
each one of plural propagation paths from the first candidate antenna
location to the first transmitter source location;
(iii) determining a signal quality change for said each one of plural
propagation paths based on predetermined signal characteristics;
(iv) determining a first receive signal quality measure at the first
candidate antenna location based on the signal quality change for said
each one of plural propagation paths; and
(v) repeating steps (i) through (iv) for each further transmit location of
the set of transmit locations to determine further receive signal quality
measures, the first and further receive signal quality measures forming a
first set of receive signal quality measures for the first candidate
antenna location;
(vi) repeating steps (i) through (v) for each remaining candidate of the
candidate antenna locations.
Description
FIELD OF THE INVENTION
The present invention relates, in general, to wireless communication
systems and, more particularly, to a method for wireless communication
system planning using ray-tracing.
BACKGROUND OF THE INVENTION
In a wireless communication system such as cellular or Personal
Communications Services, base stations are located such that radio signals
are available through out the service area. To obtain near seamless
coverage, many cells are required. Predicting the coverage of such cells
is a difficult job, and a number of tools have been developed which make
some use of terrain data, with building clutter information, such as that
available by the US Geological Survey within the United States. This data
is used in conjunction with models that are well known in the art, such as
the Longley-Rice model which uses base and subscriber heights, along with
a description of the terrain to calculate a prediction of the expected
propagation loss for the locations under consideration.
This method works sufficiently well for large cells whose base antenna is
well above the building clutter, so the influence of particular
buildings/structures or groups of buildings is minimal. When the base
station antennas are near rooftop level or below building rooftops, then
the actual size and shape of the buildings influences the signals as they
propagate down the streets and diffract around corners. These cells,
generally called microcells, typically cover a much smaller area,
especially in dense urban areas. Tools to predict micro-cell coverage
typically use information about the building sizes, shapes, and sometimes
material types to aid in modeling the propagation paths in and around the
buildings in the coverage area.
A deterministic process, as opposed to the above statistical process,
basically attempts to model the radiowave propagation as rays radiating
from the transmitter to the receiver. This approach can be effective and
accurate when the objects in the modeled environment are much larger in
dimension than the wave length of the transmitted signal. The propagation
phenomena that can be modeled in a ray-tracing process include reflection,
diffraction, transmission and the combinations of the above. Within ray
tracing there are two generally known approaches. The first is called the
"shooting-and-bouncing" method, in which a fixed number of rays are
launched from the source (transmitter), then forward-traced to follow the
different propagation paths, with a ray being terminated when it hits a
detection sphere at the receiver. A major advantage of this approach is
that it can be applied to most any type of surface. A key disadvantage is
that for every receiver location, the rays have to be launched and traced
again in all directions. This could mean hours or even days of computation
time for a practical environment.
The second method is based on image theory, which is traditionally limited
to more or less planar surfaces in the environment. The basic notion here
is that the images of a source at a fixed location in a given environment
are independent of the location of the point of observation (receiver) as
long as there are basically planar surfaces in the environment. Therefore
one can build all the images for a given location of the source and
environment and reuse it for as many receiver locations as one needs. This
represents an improvement in terms of computational efficiency, but of
course, one is limited by the planar surfaces in the environment. This is,
however, typical of an urban microcellular environment. Thus, a
conventional image theory approach may be advantageously used for
microcells, with one first determining an image tree (hierarchically
organized for ease of use) based on the location of the source in the
environment and the environment itself. The environment consists of
mirrors (or reflective surfaces) and corners. Starting from the source
image, each mirror or corner has the potential of generating a "child"
image from the source image. Each child image can further generate child
images for every mirror and every corner. Once the image tree is built,
for a given receiver location every image on the tree needs to be examined
to see whether it contributes to the total received power through a
back-tracing process from the receiver to the transmitter.
However, a key problem with image tracing is the size of the image tree for
a realistic environment, leading to very large computational and memory
requirements. The following example illustrates the problem. In an
environment defined by N mirrors, there are also (typically) approximately
N corners. Each of the N mirrors can potentially generate a reflective
image, and each of the N corners can potentially generate a diffractive
image. Without some limitation on the growth of the image tree, a source
with m levels of reflection and n levels of diffraction will generate on
the order of (2N).sup.n N.sup.(m-n) images, assuming m>n. For example, if
N=100, m=3, n=1, then a conventional image tree will include about
2,000,000 images. If each image object takes 100 bytes of memory (i.e., in
order to hold its own attributes and pointers to its ancestor image and
descendant images), the total memory needed to hold the above image tree
with fairly modest assumptions is 200 megabytes! Given the number of
images involved, it is typical for the process of determining
transmitter/receiver placements to take days or even weeks, depending on
the number of buildings or other structures, the size of the coverage
area, and the resolution of the calculated grid of predicted points.
Because of the large computational requirements in prior ray tracing
approaches, no attempt has been made to use calculated results in
determining optimal placement of anything more than single antenna sites.
However, most base stations employ more than one antenna, typically to
compensate for short or Rayleigh fading over the communication channel via
diversity. By only determining placement based on uniform propagation from
a single antenna, without consideration of possible improvements of
micro-diversity (e.g., placement to compensate for Rayleigh fading) or
macro-diversity (e.g., placement to compensate for log normal fading),
possible adjustments in the variations in the placement of diversity
antennas is foregone.
There remains therefore a need for an improved method of ray tracing which
compensates for these and other problems, including providing a
computationally efficient method for ray tracing, and using propagation
estimates from ray tracing to optimize antenna placement.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a diagram illustrating how a reflective image is generated for a
reflective surface of "mirror," and how a diffractive image is generated
for a diffractive surface or "corner," in accordance with a preferred
embodiment of the invention;
FIG. 2 is a diagram illustrating a hierarchical view of an image tree for a
given environment and source;
FIG. 3 is a diagram illustrating "back-tracing" of an image on the image
tree from the receiver to the transmitter;
FIG. 4 is a diagram illustrating the use of a scope for both a reflective
image and a diffractive image in accordance with the preferred embodiment
of the invention;
FIG. 5 is a flow diagram illustrating a method for determining an image
tree in accordance with the preferred embodiment of the invention;
FIG. 6 is a flow diagram illustrating a method for received power
estimation in accordance with the preferred embodiment of the invention;
FIG. 7 is a diagram illustrating a plot of received signal powers for a
coverage area in accordance with the preferred embodiment of the
invention;
FIG. 8 is a simplified block diagram of a processor on which the embodiment
of the invention can be practiced;
FIG. 9 is a diagram illustrating possible antenna mounting locations at a
base station;
FIG. 10 is a diagram illustrating representations of two independent
Rayleigh faded signals with approximately equal average power;
FIG. 11 is a diagram illustrating representations of the same two
independent Rayleigh faded signals of FIG. 10 but with branch imbalance,
i.e. one branch has lower average power;
FIG. 12 is a diagram illustrating representations of a cumulative
distribution of a Rayleigh faded signal which includes the cases of
selection diversity with different branch imbalances;
FIG. 13 is a diagram illustrating a representation of a cumulative Log
Normal distribution in which the Log Normal sigma is 10 dB, a value
representative of the shadow fading characteristic of urban microcells;
and
FIG. 14 is a flow chart of a process for selecting optimal diversity
antenna locations according to a preferred embodiment of the invention.
DETAILED DESCRIPTION OF THE DRAWINGS
These and other problems are solved by the method for system planning in
accordance with the invention. This method, described further below, is
particularly advantageous since most conventional ray tracing processes
use image trees consisting of large numbers of unenergized images. If one
can determine whether an image is energized or not before generating that
image, one can generate and store images for the energized portions only.
Not only is memory saved; there is also a reduction in searching time
consumed in later processing and placement determination. However, even
with such "pruning" of the image tree, there are still a fairly large
number of images on the image tree for a realistic urban environment
(e.g., 50,000). To determine the received signal for a given receiver
location, therefore an improved back-tracing process is preferably
performed for every image on the image tree. By repeating this process for
other receive locations, an estimate of the coverage quality for the given
transmitter may be obtained, from which optimal transceiver placement can
be determined. Further, where diversity reception is to be employed, a
coherent reception of all rays at each of a series of possible antenna
locations is performed, and a determination of the optimal antenna
combination is then made. All this can be performed at significant savings
in memory and processing time over prior methods due to the improvement of
the present invention.
A preferred method for determining signal propagation characteristics for
the known environment (e.g., microcell or in-building) starts with
minimizing the size of (i.e., pruning) the image tree by defining a scope
for each image on the image tree. A "scope" is defined as an angle within
which the majority of the radiated energy from the image is confined. The
source image, of course, has a predetermined scope angle-typically 360
degrees, but it could be a set lesser amount if directed (e.g., so as not
to transmit towards an immediately adjacent wall, or for sectorized
antennas). The scope angle of a reflective image, however, is usually much
less than 180 degrees. Typically, the scope angle of a diffractive image
is usually less than 45 degrees. When the image tree is built for a given
environment and source location, a scope attribute is specified every time
an new image is created. New images are only created for those mirrors and
corners that fall within the scope of an image. By defining the scope
angle and mapping out only the energized portion of the image tree, the
size of the resultant image tree is greatly reduced. This in turn saves
the memory needed to store the images, and increases the speed of
computation for received power and other data.
Subsequently, the inherent information in the hierarchy of the image tree
is preferably used to partially trace the image tree based on a received
signal level (or similar propagation/quality measure, including signal
power loss). For any image on the image tree, its reflective child image
contributes less power compared to the parent image due to the extra
reflection. The difference can be 14 dB or more for realistic
environments. On the other hand a diffracted child image typically
contributes at least 6 dB less power than the parent image, and usually a
lot less. Therefore by setting an absolute and a relative signal level
threshold, the received signal level from the current image can be
compared to the threshold and the current total received power, and
decisions made whether or not to examine the child images of the current
image. Thus, partial examination of the image tree for the computation of
the summed signal level at a given location is accomplished. This reduces
the time needed to calculate the signal power for each possible receiver
location.
In a further embodiment, the computation of the summed signal is done for a
predetermined frequency, allowing coherent addition of the various rays to
form the summed signal level. The process is then repeated for additional
candidate antenna locations. A set of summed diversity signal levels is
then formed by calculating the signal level for a given diversity system
(e.g., for 2 antenna selection, choosing the greatest summed signal level
for each possible pairing of antennas). The outcome of the different
candidate diversity placements are then compared to determine the optimal
antenna placements.
Referring initially to FIG. 1, image generation is generally illustrated,
showing how a reflective image 103 is generated on a mirror 110, and how a
diffractive image 113 is generated on a corner 120. A source (s) 101 can
create a reflective image (i) 102 behind the mirror 110, which defines the
path of the reflected ray from the source to the receiver 103 if a
receiver location is defined. Notice that the location of the image 102 is
independent of the location of the receiver 103. A source (s) 111 can also
create a diffractive image (i) 112 at a diffracting corner 120, which
defines a diffracted path from the transmitter to the receiver 113 if the
location of the receiver is defined. Again, the location of the
diffractive image 112 is independent of the location of the receiver 113.
FIG. 2, generally depicts the hierarchy of an image tree (generally
designated 200). For a given environment and a given source location, the
source 201 can generate for every mirror in the environment a reflected
child image, and for every corner in the environment a diffractive image.
These are called first generation (or first order) images 210. Each first
generation image can in turn act like the source image and generate for
every mirror in the environment a reflected child image, and for every
corner in the environment a diffractive image. These are called second
generation or second order images 220. This process can be repeated for
second and higher order images and stopped after a predetermined number of
reflections and diffractions is reached. The images generated in this
process are then linked together to form a hierarchical image tree 200.
If the given transmitter TX is the head of the image tree, i.e. the source
201, and A is an image somewhere in the tree, whether reflective or
diffractive, and if B is a reflective child of image A, then the power
contribution from image B is less by an amount equal to the loss due to
the reflection plus the extra free space loss below that from image A. For
real environments this is at least 14 dB plus the difference in free space
loss (determined based on the path segment length from a
reflective/diffractive surface corresponding to image A and a reflective
point on the surface corresponding to image B--e.g., the distance between
r1 312 and r2 322 of FIG. 3). In the same way, if C is a diffractive image
of A, then the power contribution of image C is a loss of at least 6 dB
(usually much more than 6 dB) plus the difference in free space loss below
that of image A. If the power contribution of image A is already below a
given threshold, then there is no need to check B and C and their siblings
and descendants, so the image tree can be further pruned.
Turning to FIG. 3, "back-tracing" of an image is illustrated, i.e., tracing
an image tree back from the receiver 331 to the transmitter 301. When the
receiver's location is known, each of the images on the image tree may be
examined to see whether it lies on a propagation path between the
transmitter 301 and the receiver 331. This is done by back-tracing.
Starting from the receiver (RX) 331, a propagation line is first drawn
between the second generation image (aa) 321 and RX 331, from which a
point of reflection (r2) 322 on the surface (mirror 320) is found. If r2
322 is not on mirror 320 or the line-of-sight (LOS) path between RX 331
and r2 322 is blocked, then this image does not constitute a possible
propagation path. Otherwise a line or ray is drawn between r2 322 and (a),
which is the parent image 311 of the image aa 321. Another point of
reflection (r1) 312 on surface 310 is then found. Again, if (r1) 312 is
not on mirror 310 or the LOS path between (r2) 322 and (r1) 312 is
blocked, then this image 311 does not constitute a valid propagation path.
If the LOS clearance between (r1) 312 and the source (s) 301 (which is the
parent image of the image a 311) exists, then there is a propagation path
from the source 301 to the receiver RX 331 through two reflection points
312, 322.
FIG. 4 generally depicts how a scope can be used in building an image tree
in an urban canyon 400. The scope is an angle that defines the energized
region of space based on possible propagations from the image. In the case
of building 410 having surface 415 (which acts as a mirror in this case),
image i.sub.1 411 is the image for any reflections off surface 415 from
transmitter source location 401. However, since image i.sub.1 411 can only
serve as an image for those rays propagating from surface 415 within the
region defined by the scope 412, scope 412 can be used to significantly
reduce the possible daughter images of image i.sub.1 411 (i.e., to those
images having reflection or diffraction points within the region defined
by scope 412). Similarly, in the case of building 420 having surface edge
425 (which acts as a diffraction corner), image i.sub.2 421 is the image
for any diffractions off surface 425 from source 401. However, again the
image i.sub.2 421 can only serve as an image for those rays propagating
from edge 425 within the region defined by the scope 422, and scope 422
can be similarly used to significantly reduce the possible daughter images
of image i.sub.2 421 (i.e., to those images having reflection or
diffraction points within the region defined by scope 422). In both cases,
the scope angles of reflected and diffractive images are much smaller than
360 degrees, which would conventionally be required for the two
dimensional case. By only creating child images for surfaces (e.g.,
mirrors and corners) that are within the scope of the current image, the
growth of the image tree will be limited within the energized portion of
the potential image tree. This will effectively "prune" the image tree to
a manageable size for a realistic environment such as urban microcellular
applications.
Next, FIG. 5 generally illustrates a method 500 by which a pruned image
tree can be built. First, if it has not already been determined, the
location of all significant structures (buildings, towers, terrain, etc.)
and any desired structural characteristics (e.g., the location of each of
plural reflective surfaces (defining all potential reflective points on
the surface) and plural diffractive surfaces, along with signal power loss
characteristics) are determined. Then, for a given transmitter source
location and known environment (i.e., the structural characteristics, the
source is set as the current image, and its predetermined scope set
(typically to 360 degrees)) (step 510). Then, for every "mirror" that is
in the environment (step 520) a determination is made whether the mirror
is partially or completely in the scope (e.g., unobstructed LOS view) of
the current image (step 530). If it is, a child image (or first order
reflective image) is determined (step 540) (via conventional trigonometry)
for that mirror and the scope computed. Next, for every "corner" in the
environment (550) a determination is made whether the corner is in the
scope of the current image (step 560). If it is, a child image (or first
order diffractive image) is determined for that corner and the scope
computed (step 570). Finally, the process is repeated for a predetermined
number of reflections and diffractions for each branch. For example, if
two levels of reflections and three levels of diffractions are desired,
the image tree would be five levels deep at that branch; however, all
third level reflective images (and their child images), even if third
order images, would be pruned. In less dense urban environments, where
there will be greater energy loss per reflection/diffraction due to, among
other factors, longer paths, determination of two levels of reflection and
three levels of diffraction are generally sufficient to adequately model
the actual propagation environment. In denser environments an additional
level, or perhaps even more, may be necessary. One skilled in the art will
appreciate how to choose an appropriate level of image generation based on
the environmental density, as well as considerations such as the available
memory/processing power (which increase dramatically as each additional
level is added).
Finally, FIG. 6 generally illustrates a preferred method (600) for
back-tracing the images on the image tree and determining a received
signal quality measure (e.g., the total received power, propagation loss,
etc.) for given receiver locations. First, the process is initialized by
starting with the top of the image tree, the transmitter (610). A
back-tracing process (such as illustrated in FIG. 3) is performed to
determine whether the first receiver location is in a direct line of sight
with the transmitter (620). If it is, the path from the transmitter to the
receiver forms a first propagation path, and the signal quality change
(e.g., power loss, attentuation, or change in another quality measure)
across the path, in this case due solely to free space path loss, is
determined (630). If not, the next image, preferably in descending order
on the image tree, is set as a current image (640).
Thus, following the transmitter the contribution of child image A (of FIG.
2) would be determined (620). If image A is a diffractive image, this
would be determined as for the transmitter, i.e., whether a direct line of
sight between the image (which is co-located with the diffractive surface)
and the receiver exists. If image A is a reflective image, step 620
determines whether the line defined by image A and the receiver is
unobstructed between the reflective surface of image A and the receiver
(the intersection of this line and the reflective surface defining the
reflective point) and whether the line from the reflective point to the
transmitter is unobstructed--in other words, whether a propagation path
exists using image A. If there is a propagation path, a power loss
estimation is performed by determining each contribution--i.e., the free
space loss across the two path segment lengths and a reflective surface
loss. The reflective surface loss may be defined as a set value for all
reflective surfaces (e.g., 14 decibels) for simpler calculations;
alternatively, where known the reflective characteristics of the
materials/structure of each reflective surface may be used, even including
an angular factor for certain rough/irregular surfaces, so that a more
precise power loss estimation may be obtained. Similar diffractive
characteristics may by used, along with the angle of diffraction, in
calculating power loss around diffractive surfaces.
Following step 630, a comparison is made between the power contribution
(e.g., initial transmit power times power loss) of the current propagation
path and a predetermined threshold. The threshold is preferably set low
enough (e.g., a 120 dB drop from the transmit power) to exclude de minimus
contributions. If the power contribution of, say image B of FIG. 2, were
less than the threshold, no determination of the contribution of its child
images (e.g., image D) would be made, since such would necessarily be de
minimus too. In this case, the next sibling or same-order image would be
set as the current image (e.g., image C) (step 660), and the process
repeated. Additionally, the power contribution is also preferably compared
against the difference between the cumulative power contributions already
determined and a relative threshold (e.g., 20 dB) to exclude de minimus
relative contributions; this is useful, e.g., in excluding contributions
above the first threshold but still de minimus when short propagation
paths with little loss are also present. If the power contribution is
greater than both thresholds, then a determination of the contribution of
each child/lower order image is then made (670).
This process is repeated (steps 541, 571) until all images on the tree have
been examined or excluded by a threshold determination, yielding a
received signal quality measure (e.g., received power) cumulated during
step 630.
This process is then repeated for all predetermined receiver locations,
yielding an estimate of the signal propagation characteristics within the
predetermined region of interest of the given transmitter. The region of
interest for microcellular systems will be defined typically as all
regions within a predetermined radius of the transmitter location that are
external to the structures within the region (internal calculations could
also be performed, but would require more complex calculations involving
power loss based on penetration characteristics). For in-building systems,
the region of interest would be limited by the building boundaries. One
skilled in the art will appreciate that the accuracy of the signal
propagation characteristics is dependent on the number/distance between
receiver locations, and how to select an appropriate number based on a
balance between factors such as the accuracy desired and the computational
capacity/time available. In both cases, the signal power for each receiver
site can be used in a variety of ways to determine system planning, for
example by factoring together for some overall measure of coverage
quality, outputting the receiver locations and powers below a desired
received signal level (indicating shadowing or cell boundaries), and
displayed for a user so as to show relative receiver powers.
This latter approach may be advantageously used in determining the
placement for a receiver, such as a wireless fixed access unit (WAFU) for
use in PCS (personal communication services) systems. This is illustrated
in FIG. 7, where one transmitter location 701 is used to cover a local
region 700 such as a residential neighborhood with plural houses 710, 720.
Relative received powers is determined for different possible receiver
locations, illustrated by areas 711-713 and 721-722. The size of such
areas may vary depending on the placement accuracy desired. Further, for
ease of determination only relative power levels are displayed (in this
case on a scale of 1 to 10, although any scale could be used, including
color coding on a computer display). This is sufficient to determine the
placement of a WAFU in area 711 adjacent building 710. In cases where the
relative display does not provide enough detail, such as for areas 721 and
722 adjacent building 720, which both show a relative power of 3, the
actual determined received signal powers in both areas can be displayed so
the optimal area can be selected. Finally, where multiple transmitter
source locations are possible, e.g., site 702, the entire process can be
repeated to determine the receive powers throughout the coverage area
based on use of the second transmitter location 702. The results may be
compared in a variety of ways, two such being either comparing the
coverage for known receiver/subscriber locations (e.g., if only buildings
710 and 720 were likely subscribers, TX 702 would be the preferred site),
or assigning an overall coverage rating for the region 700. This latter
approach could be realized in many ways, too, including a simple sum of
all the receive signal powers in the region for each transmitter and
comparing the sums, a determination of the percentage of receive areas
that fall below a minimum desired signal power for each transmitter and
comparing the percentages, etc.
FIG. 8 illustrates a simplified block diagram of a processor on which the
methods according to the various embodiments of the invention can be
practiced. In this case the processor is a general purpose computer 810
having a central processing unit (CPU) 811 coupled via bus 812 to RAM
(random access memory) 813, user I/O (input/output) 814, and memory
control unit 815. Memory control unit 815 is in turn coupled to a main
memory 816 in which separate databases are stored, e.g., object location
data 817 (i.e., the locations of and characteristics of edges and walls in
the region), image tree data 818 (including the pruned image tree listing
for each calculated location), and calculated signal level data 819. While
it is expected that with current technology a general purpose computer
will be required to perform the various embodiments of the invention, one
skilled in the art will appreciate that as processing powers increase one
will be able to utilize any automated digital processor, e.g., ASIC's
(application specific integrated circuits), DSP's (digital signal
processors) and the like.
A further embodiment of the invention, calculating optimal diversity
antenna placement, may now be understood with reference to FIGS. 9-14.
This embodiment takes advantage of the fact that after ray-tracing is
completed, the rays available at or near the receiver can be added in
several ways to calculate the expected average power, or to calculate the
local mean power, or the coherent signal power as desired. In each case
these values are different kinds of estimates of the power that would
actually be received by an antenna, since the environment is only a
computer model and does not include every detail of the actual
environment. These estimates however can be very good, and can be
advantageously used to predict the performance of the actual signal
values. The method of adding signals coherently implies that both the
amplitude and phase of each ray is known (or can be predicted accurately,
given a transmission frequency and propagation distance). Although there
generally will be some error in both of these parameters, the resulting
coherent addition of rays will still provide a good representation of the
actual signals in the environment being modeled.
This embodiment has particular applicability to microcellular environments.
Generally, when base antenna locations are selected for large cells, it is
sufficient to only specify the height and separation of the diversity base
receive antennas since there are no close-by obstructions that will
interfere with the field of view of the antennas at a tall site. However
for microcell sites, which are often below rooflines, etc., the antenna
separation can produce an important engineering trade-off. One possible
trade-off is illustrated in FIG. 9, i.e., increasing the separation around
a side of a building between two diversity antennas.
When the antennas are within the local clutter, generally two things
happen. First, the scattered fields are more randomized near the base
antennas producing a sufficiently low statistical correlation coefficient
(a quantity typically used to rate the diversity effect that the antennas
will be able to use), at a relatively small separation distance. This will
give good diversity performance with an antenna separation distance of
generally less than 10 wavelengths, whereas a macrocell that is in a more
open environment, above the building rooftops, requires an antenna
separation distance of perhaps closer to 10-20 wavelengths, depending on
the environment, to achieve the same degree of signal decorrelation.
Second, with a microcell located below the rooftop, the path from
subscriber to base is much more susceptible to become shadowed by
obstructions, thus limiting the potential coverage area due to the
site-specific characteristics of the particular cell. If, however, the
base antennas are sufficiently well separated, the effect of the shadowing
of the signal due to obstructions can be reduced, since at least one base
antenna may be in a location that does not experience significant
shadowing. This, however, introduces average signal imbalance to the two
antennas since one antenna would receive a higher average signal than the
other in this condition. A trade-off is thus presented. If branch
imbalance exists, the beneficial effect of diversity is reduced; however,
by using large antenna separations, the signal coverage in the cell may be
improved (even after branch imbalance is considered) in areas where
shadowing may have previously limited the coverage for small separations.
Thus, by trading-off micro-diversity improvements to Rayleigh fading
(i.e., coherent multipath fading produced from localized scattering),
improvements can be made by the use of macro-diversity to help mitigate
log normal or shadow fading (e.g., blockage or attenuation of the signal
due to large obstructions or terrain effects).
The actual signal improvement in a coverage area due to the signal being
received at diversity antennas is a combination of the effects of
macro-diversity and micro-diversity. Thus, to choose diversity antenna
locations for a microcell base station where these two effects are
significant would require a careful analysis, including estimates of both
multipath fading and shadow fading for the various locations throughout
the coverage area. In cases in which digital receivers are used, the
effects of delay spread, etc. could be considered as part of the
performance criteria. By using a ray-tracing analysis according to the
invention, these parameters can now be considered in a computationally
efficient manner so as to improve the selection process of the diversity
base antenna locations.
Turning now to FIG. 9, two buildings are shown with diversity antennas of a
communication unit 820 mounted. In the first example, antennas 821, and
822 are shown on the same side of the roof of a building with a given
separation distance. In the second example, antennas 823, and 824 are
mounted on different sides of the roof of a building, and their locations
are shown by 831 and 839, respectively. Other possible locations for these
antennas are shown by 832-838. These points represent examples of possible
test locations for these antennas to be used in an analysis to determine
an improved mounting location for the antennas based on criteria defined
herein (similar, e.g., to the test points shown adjacent to the buildings
710 and 720 of FIG. 7). Generally, one antenna, e.g., 823, may be fixed to
point 831, and the other antenna, 824 would be moved to various test
locations during the analysis. This method is preferred to save time,
although both antennas could be moved if desired. Other test points could
also be defined at any location inside or outside the building, or on
another building or structure. The goal of the analysis will be to
determine an improved set of locations which will give better performance
across the desired coverage area.
Referring now to FIG. 10, a plot representing a Rayleigh fading profile is
shown. The fading profile from two separate antennas can be seen, each
receive signal 841, 842 appearing to be independent of the other, with
approximately the same average power in each. The profile describes the
variation in the power of the envelope of the signal measured in dB
(decibels) as the subscriber moves a short distance, in this case
approximately 5-10 wavelengths. Although Rayleigh fading is shown here,
other distributions could be considered in the analysis, including Rician
(which can look similar in some cases). This signal profile represents the
variation that is generally seen in cluttered micocells since the
transmitted signal reflects off numerous objects and adds up coherently at
the receiver, producing a composite signal that varies in amplitude and
phase at the carrier frequency.
FIG. 11 shows the same type of Rayleigh profile, but in which a first
branch receive signal 846 (counterpart to 841 in FIG. 10) has less average
power than the receive signal 847 of a second branch. This situation is
generally referred to as branch imbalance, and is specified by a dB value
representing the ratio of the difference of the two powers. Branch
imbalance is typically produced in a radio system when one antenna is
blocked or shadowed compared to the other, thus introducing additional
attenuation in the path of the antenna that is blocked. This affects the
average of the power, but does not in general affect the multipath fading
distribution of the transmitted signal.
FIG. 12, generally depicted as 850, illustrates the cumulative distribution
of a Rayleigh fading process where the probability of the fade depth being
at least the number shown on the abscissa is plotted on the Y axis. Curve
851 indicates the result from a single branch Rayleigh fading random
process, and would be representative of the multipath fading over much of
the cell area. As characteristic of a Rayleigh fading distribution, the
probability of a 10 dB fade is approximately 10% and a 20 dB fade is
approximately 1%. Curve 855 shows the case of equal branch selection
diversity. Although selection diversity is illustrated here, one skilled
in the art will readily appreciate how to apply the invention to this or
other types of diversity, e.g. max-ratio, equal gain, switched, etc.,
depending on the system design. As seen in 855, the probability of a 10 dB
fade is improved to 1% for selection diversity. For example, with
independent branches, the probability of a 10 dB fade will be the
probability that both branches fade at least 10 dB at the same time, and
this will be given by P(10 dB)selection=P(10 dB)*P(10 dB)=0.1*0.1=0.01=1%.
This is a significant improvement, and is the reason that most radio
systems make use of diversity.
However, if the diversity branches are not balanced, this improvement is
somewhat degraded. Curve 854 represents selection diversity with 3 dB of
imbalance between the two branches. Curve 853 has 6 dB of branch
imbalance, and 852 has 9 dB of branch imbalance. Even with 9 dB of branch
imbalance, there is still worthwhile improvement in the use of diversity
as shown.
FIG. 13 shows the cumulative distribution of a Log Normal fading process
where the probability of the fade magnitude being less than or equal to
the number shown on the abscissa is plotted on the Y axis. The Log Normal
fading model is indicative of the composite attenuation in the signal
caused for example by shadowing or blocking from clutter in the
environment. Typically, as a subscriber moves a distance that is close to
the average size of the buildings, the Log Normal fading process becomes
decorrelated, giving a user a different value of shadow fading. In a
below-roofline urban microcellular environment, the variation of the
shadow fading is typically in the range of a standard deviation .sigma.=10
dB. This implies that the total variation, independent of distance from
the site, can vary +/-3 .sigma. or -30 to +30 dB. Because this is somewhat
more than the variation expected from the Rayleigh fading process, it
should be considered in the selection of antenna locations--since the
shadowing to one side of a building can be totally different than the
shadowing to the other side of a building where the antennas could be
mounted.
By using a ray-tracing analysis to calculate the expected propagation path
between the subscriber and each base antenna, the effects shown in FIGS.
12 and 13 are properly calculated for each path.
Referring now to FIG. 14, a description of the process steps according to
this further embodiment is shown. The process begins in block 861 with the
selection of a set of candidate antenna locations for which to analyze the
performance of the cell. This is illustrated by locations 831-839 of FIG.
9, which are examples of possible antenna locations. As an example of the
selection of antenna locations, the analysis could begin with antennas at
locations 831 and 832. After the first run, the analysis could shift the
location of the second antenna to 833. This process could continue through
all selected locations 831 through 839. To simplify the process the
location of the first antenna is preferably not changed (e.g., a
predetermined group of combinations being defined, responsive user input
parameters, as each combination including location 831), but any
combination of locations can be used. If additional site regions are to be
compared, the overall process is repeated for an additional set of
candidate antenna locations.
Block 862 describes breaking up the cell into small test areas (i.e., a
group of locations within the overall coverage area, further narrowed to a
smaller set of transmit locations when less than the full group/coverage
area is to be tested--such as by excluding locations in building
interiors). This can be defined by the user to be a linear drive route, or
a square grid of predefined size (e.g., as is illustrated in FIG. 7). By
using small test areas, an analysis can be performed for each area, and
this will make coverage comparisons easier to make.
Block 863 performs a ray-tracing prediction for each point in the test
area, which can be a matrix of points, or a linear segment of points. A
separate calculation is made for each candidate antenna location using
every point (i.e., the set of transmit locations) in the test area, to
arrive at a set of receive signal quality measures. The ray-tracing
prediction includes the coherent addition of rays such that the fast
fading multipath variations (e.g., see FIG. 10) are obtained from the
calculations. This is preferably accomplished by determining each
propagation path distance, and using predetermined signal characteristics
such as a given frequency and transmit power. While the frequency should
remain the same for each cycle, in an alternative embodiment the transmit
power is adaptive for each transmit location as to arrive within a set
receive signal strength range at the candidate antenna locations, up to a
maximum transmit power, thus more closely approximating actual subscriber
power control/transmit behavior.
Block 864 evaluates the result of the signal variations as a receiver
coupled to the diversity antennas would be able to interpret it for each
test area. The quality factor mentioned here is a diversity signal quality
measure, which is derived by one of several different types of diversity
processing analyses, depending on the receiver diversity design choice.
Such preferably includes a statistical cumulative distribution (over the
entire cell) of signal quality estimates after the use of diversity is
included. For example, where the diversity receiver uses diversity
selection, then the greatest of the receive signal quality measures for
each transmit location is used; if diversity combining is to be used, all
measures would be combined according to the design algorithm. From this
process a set of diversity signal quality measures is derived for each
combination of candidate antenna locations.
From this set an overall coverage quality measure is determined for the
combination of candidate antenna locations. This coverage quality measure
is derived using predetermined coverage criteria, which will vary
according to the system design choice. For example, in a first alternative
a histogram of C/(I+N) (carrier to interference plus noise) ratio is
generated, using a selected I+N value for the combination. From this
histogram an overall coverage rating is determined, e.g., by (1) summing
all values, (2) determining a percentage of transmit locations (either in
the whole coverage area or within one or more selected range(s) from the
antennas) below a set C/I+N ratio, (3) selecting all histogram values
having a worse 10% (or other selected) value, etc. A second alternative
includes the use of an algorithm modeling a more complex radio modem to
interpret the predicted profiles and generate a quality metric which is
proportional to BER (bit error rate) or WER (word error rate) for each
profile that is tested. This radio modem analysis is preferably done by
simulating the radio by a computer program or by generating a time varying
RF (radio frequency) signal that represents that of the predicted signal
for a test route, and applying this to a radio from which actual
performance can be measured. A third alternative is to select a BER or WER
from a curve representing the radio performance as a function of signal
level, and for each signal level obtained by the prediction, tabulate the
effective result of the cumulative average of the BER or WER of the signal
profile.
In Block 865, the cell area can be found in different ways based on the
analysis desired by the user. If the analysis includes multiple cells, the
coverage of the cell is selected, e.g., as a best server area. This is an
area defined by those locations that are best served by that cell where
the signal is above a minimum threshold. Thus, the coverage area can
include coverage holes, and its size may be dictated by how much signal is
at the edge of the cell. If there are no other cells in the analysis, the
coverage area can be fixed by the user, or can be calculated based on
signal level or C/N (carrier to noise level). After the evaluation is
made, the statistics are preferably outputted to a user (e.g., printed
out) for the given combination of base antenna locations.
In Block 866, this process is repeated for each set of possible base
antenna locations, and the results for each combination compared. The
combination with the greatest coverage quality measure are then output in
block 867, e.g., by suitable output to a user, or storage of the locations
of the optimal antenna combination.
Thus, it will be apparent to one skilled in the art that there has been
provided in accordance with the invention, a method and apparatus of image
tree generation, pruning, and antenna placement that fully satisfies the
objectives and advantages set forth above. While the invention has been
described in conjunction with specific embodiments thereof, it is evident
that many alterations, modifications, and variations will be apparent to
those skilled in the art in light of the foregoing description.
Accordingly, the invention is intended to embrace all such alterations,
modifications, and variations within the spirit and scope of the appended
claims.
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